Papers
4
Total Citations
83
H-Index
3
About
Nazmus Sakib is a robotics researcher whose work focuses on creating safe, socially-aware autonomous navigation systems. His most impactful contribution, "Mapless Navigation among Dynamics with Social-safety-awareness" (71 citations), introduces a novel reinforcement learning approach that uses 2D laser scans for collision avoidance in human-populated environments. This work uniquely distinguishes between "ego-safety" (collision risk from the robot's perspective) and "social-safety" (the robot's impact on surrounding pedestrians), addressing a critical gap in human-robot interaction. Sakib has also explored autonomous driving, developing a Deep Reinforcement Learning-based motion planner for highway lane changes under uncertainty. Earlier in his career, he contributed to space robotics by building a simplified semi-autonomous Mars Rover (IUT Mars Rover), successfully tested at the European Rover Challenge 2015, and worked on low-cost EMG signal recorders for prosthetic arm control in developing countries. His research demonstrates a consistent commitment to making autonomous systems safer, more socially aware, and accessible—bridging the gap between theoretical robotics and real-world deployment in dynamic, unpredictable environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2An approach to build simplified semi-autonomous Mars Rover5 citations · 2016
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